How AI Can Help You Build Better Products: 10 Tools To Save You Time


AI is everywhere: from self-driving cars to text generation, to creating images and music. So, how can product managers use AI to save time and build better products? AI can help with research, feedback management, user engagement, and roadmapping. With AI, product managers can work faster and smarter. In this guide, we’ll show how product managers can use AI to build better products.

10 Rules for Managing Apache Kafka

Without proper guidance, it’s easy to miss out on Kafka’s full capabilities. While not the easiest technology to optimize, Kafka rewards those willing to explore its depths. Under the hood, it is an elegant system for stream processing, event sourcing, and data integration. Download this white paper to learn the 10 critical rules that will help you optimize your Kafka system and unlock its full potential.

12 Reasons Snowflake Costs Get Out of Control — And How to Solve It

With no barriers to entry you can get started with Snowflake for next to nothing, but as you may already know, costs can quickly spiral out of control. While usage costs can be better managed for your internal BI use case, Snowflake costs skyrocket for SaaS providers because the need to deliver real-time, interactive analytics in a multi-tenant environment is always on.

Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Reimagined: Building Products with Generative AI

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.

10 Rules for Managing Apache Cassandra

It’s no surprise that Apache Cassandra has emerged as a popular choice for organizations of all sizes seeking a powerful solution to manage their data at a scale—but with great power comes great responsibility. Due to the inherent complexity of distributed databases, this white paper will uncover the 10 rules you’ll want to know when managing Apache Cassandra.

Top 5 Challenges in Designing a Data Warehouse for Multi-Tenant Analytics

Multi-tenant architecture allows software vendors to realize tremendous efficiencies by maintaining a single application stack instead of separate database instances while meeting data privacy needs. When you use a data warehouse to power your multi-tenant analytics, the proper approach is vital. Multi-tenant analytics is NOT the primary use case with traditional data warehouses, causing data security challenges.

Entity Resolution Checklist: What to Consider When Evaluating Options

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

The Complete Guide to Managing User Feedback


To build better products, you need to listen and act on user feedback. Having an effective feedback management system can help! It can help you not only build the right features, but also avoid wasting time and resources. This guide sheds the light on all that and more. Discover ways of consistently gathering user feedback, prioritizing ideas, planning your roadmap, and closing the feedback loop.

7 Pitfalls for Apache Cassandra in Production

Apache Cassandra is an open-source distributed database that boasts an architecture that delivers high scalability, near 100% availability, and powerful read-and-write performance required for many data-heavy use cases. However, many developers and administrators who are new to this NoSQL database often encounter several challenges that can impact its performance.

How Embedded Analytics Helps Product Managers Exceed Their KPIs

Embedded analytics can help you deliver cutting-edge analytics experiences to your end-users that align with KPIs that are critical to the growth and success of your business. Read this eBook to learn how an embedded analytics platform, like Qrvey, can help PMs exceed the following KPIs: Growing revenue while improving customer retention rate Delivering rapid time to value Earning a high net-promoter score Increasing Gross Margin / Profitability Conversion rate from trial to paid Don’t just meet

The Definitive Entity Resolution Buyer’s Guide

Are you thinking of adding enhanced data matching and relationship detection to your product or service? Do you need to know more about what to look for when assessing your options? The Senzing Entity Resolution Buyer’s Guide gives you step-by-step details about everything you should consider when evaluating entity resolution technologies. You’ll learn about use cases, technology and deployment options, top ten evaluation criteria and more.

How to Migrate From DataStax Enterprise to Instaclustr Managed Apache Cassandra

If you’re considering migrating from DataStax Enterprise (DSE) to open source Apache Cassandra®, our comprehensive guide is tailored for architects, engineers, and IT directors. Whether you’re motivated by cost savings, avoiding vendor lock-in, or embracing the vibrant open-source community, Apache Cassandra offers robust value. Transition seamlessly to Instaclustr Managed Cassandra with our expert insights, ensuring zero downtime during migration.

“Build vs Buy Analytics?” The Question ALL SaaS Leaders Need to Answer in 2024

As a SaaS leader, you know that the more metrics, insights, and analytics you add to your products, the more engagement you’ll have – and the stickier your product will become with customers. At what point do you decide to keep building your analytics in-house or invest in an embedded analytics solution? Read our Build vs. Buy Analytics guide to learn: Top 4 benefits of embedded analytics A quick cost comparison of in-house analytics development vs embedded analytics 10 considerations to help yo

Use Cases for Apache Cassandra®

There’s a good reason why Apache Cassandra® is quickly becoming the NoSQL database of choice for organizations of all stripes. In this white paper, discover the key use cases that make Cassandra® such a compelling open source software – and learn the important pitfalls to avoid. From understanding its distributed architecture to unlocking its incredible power for industries like healthcare, finance, retail and more, experience how Cassandra® can transform your entire data operations.

How to Deliver a Modern Data Experience Your Customers Will Love

In embedded analytics, keeping up with the pace of innovation is challenging. Download Qrvey's guide to ensure your analytics keep pace so you can solve your user's biggest challenges, delight them, and set your product apart from the competition. The guide outlines how to use embedded analytics to: Increase user satisfaction Go to market faster Create additional opportunities to monetize your product It also shares what to look for to ensure your embedded analytics are keeping up with the lates

How to Leverage AI for Actionable Insights in BI, Data, and Analytics

In the rapidly-evolving world of embedded analytics and business intelligence, one important question has emerged at the forefront: How can you leverage artificial intelligence (AI) to enhance your application’s analytics capabilities? Imagine having an AI tool that answers your user’s questions with a deep understanding of the context in their business and applications, nuances of their industry, and unique challenges they face.

Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.

1 2 3 4 5 6 7